The field of the invention is systems and methods for magnetic resonance imaging (“MRI”). More particularly, the invention relates to systems and methods for jointly estimating image degradation and reconstructing images in which the image degradation is mitigated.
One of the main goals of MRI is to obtain an accurate representation of the object or to estimate underlying physiological quantities of interest. However, there are many factors that degrade (e.g., blur) the images obtained with an MRI system and, therefore, inhibit the image reconstruction or parameter estimation tasks. The sources of these degradations can be external or internal with respect to the imaged object. External sources of degradation, such as imperfections in the magnet or electronics, can be addressed by improved hardware designs. However, internal sources result in degradations that are more conspicuous and challenging to correct.
Physiologically induced internal sources are often times (directly or indirectly) related to the quantities to be estimated. For example, there are instances in which it is desired to estimate the various chemical content within a slice in the object of interest; however, this spatially varying chemical content induces a spatially varying resonance profile. Under rapid imaging conditions, this spatially varying resonance profile causes very serious artifacts including signals being completely shifted from their true position. This is but one example where the quantities to be estimated (chemical content) directly relate to the artifacts produced in the images. There are also many situations where the artifacts are indirectly related to the quantities that it is desired to estimate.
One such example is in the area of functional MRI, or fMRI. In this imaging application, the objective is to estimate signal levels due to neuronal activation in certain regions in the brain. Neuronal firing, however, is also correlated with increased blood volume, which in turn changes the local magnetic field in that region. These field inhomogeneities often cause signal loss, thereby confounding the task of interest. In general, such susceptibility-induced field inhomogeneities arises at tissue boundaries, such as between airbone interfaces (brain) and airtissue interfaces (brain, intestines, lungs). These inhomogeneities result in images with severe artifacts, including signal loss and geometric distortion or warping. Other examples of internal degradations include blood flow effects, which introduce motion artifacts completely overriding the signal of interest. In that context, estimating the speed of the blood (or the flow) becomes a daunting task, where the artifacts are a function of the parameters to be estimated.
The foregoing examples describe situations in which the degradations arise during the acquisition process. The challenge with these situations is that the degradations are often unknown; thus, it is unknown how to invert them. In addition, the degradations severely affect the measurements, thereby making it difficult to estimate the degradations from the data itself. All this in turn directly or indirectly inhibits the image reconstruction or parameter estimation task of interest.
In order to solve this class of problems, the traditional approach has been to either fine tune the acquisition parameters so that the obtained images are as “artifact-free” as possible, or to design post-processing methods that correct for those artifacts using some estimates or prior assumptions about the inherent degradations. These methods are either sub-optimal or impractical and, therefore, there remains a need to provide a method for reconstructing quality images in the presence of significant degradation sources.